class_docstr: An abstract class for implementing scalable GNNs via historical
  embeddings.
  This class will take care of initializing :obj:`num_layers - 1` historical
  embeddings, and provides a convenient interface to push recent node
  embeddings to the history, and to pull previous embeddings from the
  history.
  In case historical embeddings are stored on the CPU, they will reside
  inside pinned memory, which allows for asynchronous memory transfers of
  historical embeddings.
  For this, this class maintains a :class:`AsyncIOPool` object that
  implements the underlying mechanisms of asynchronous memory transfers as
  described in our paper.
  
  Args:
      num_nodes (int): The number of nodes in the graph.
      hidden_channels (int): The number of hidden channels of the model.
          As a current restriction, all intermediate node embeddings need to
          utilize the same number of features.
      num_layers (int): The number of layers of the model.
      pool_size (int, optional): The number of pinned CPU buffers for pulling
          histories and transfering them to GPU.
          Needs to be set in order to make use of asynchronous memory
          transfers. (default: :obj:`None`)
      buffer_size (int, optional): The size of pinned CPU buffers, i.e. the
          maximum number of out-of-mini-batch nodes pulled at once.
          Needs to be set in order to make use of asynchronous memory
          transfers. (default: :obj:`None`)
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code: def forward_layer(
      self,
      layer: int,
      x: Tensor,
      adj_t: SparseTensor,
      state: Dict[str, Any],
  ) -> Tensor:
      raise NotImplementedError
  
file_path: DIG/dig/lsgraph/method/GraphFMOB/models/base.py
imports: from typing import Optional, Callable, Dict, Any
  import torch
  from torch import Tensor
  from torch_sparse import SparseTensor
  from dig.lsgraph.method.FM import FeatureMomentum
  from ..pool import AsyncIOPool
  from ..loader import SubgraphLoader, EvalSubgraphLoader
label_desc: Use image node_id 10 for calling the ScalableGNN obj's underlying member method code with example usage: obj.forward_layer(layer, x, adj_t, state) without return types